Going from CER to Patient-Centered Care: Implications of Heterogeneity (Text Version)

Slide presentation from the AHRQ 2010 conference.

On September 28, 2010, David Atkins made this presentation at the 2010 Annual Conference. Select to access the PowerPoint® presentation (422 KB). Free PowerPoint® Viewer (Plugin Software Help).


Slide 1

Atkins 2 Slide 1. Going from CER to Patient-Centered Care: Implications of Heterogeneity

Going from CER to Patient—Centered Care: Implications of Heterogeneity

  • Trial: Is treatment A better than treatment B?
  • Clinician: Is treatment A better than B for this specific patient?
  • Health care system: Is treatment A better than B, and for whom, in which settings?

Images: The Department of Veterans Affairs and Department of Defense logos. These two logos appear on all subsequent slides.
 

Slide 2

Atkins 2 Slide 2. Heterogeneity and Policy

Heterogeneity and Policy

  • Policies seek to promote use of "best" treatment option.
  • "Best" treatment for population may not be same as that for individuals.
  • Most important when variation is:
    • Common.
    • Leads to big enough differences to change decision making.
    • Treatment choices can't be adjusted.

Slide 3

Atkins 2 Slide 3. Audience Response

Audience Response

  • Is CABG the best option for all patients with diabetes?
  • How might a health system encourage greater use of CABG in appropriate patients?
  • Would it be appropriate to discourage CABG in groups where PCI produces equivalent outcomes?

Slide 4

Atkins 2 Slide 4. Is CABG the Best Choice for Patients with Diabetes?

Is CABG the "Best" Choice for Patients with Diabetes?

  • Need to consider harms and complications.
  • Patient preferences for different outcomes:
    • e.g., short-term risks of CABG.
  • Variation due to quality of surgeon:
    • Applicability of trial evidence.

Slide 5

Atkins2 Slide 5. Policies Used To Influence Use of Best Treatments

Policies Used To Influence Use of "Best" Treatments

  • Guidelines
  • Audit and Feedback
  • Coverage decisions:
    • Non-coverage
    • Conditional coverage
    • Tiered coverage
  • Quality Measurement:
    • Incentives, Public reporting

Slide 6

Atkins 2 Slide 6. Distinguishing Important from Unimportant Heterogeneity

Distinguishing Important from Unimportant Heterogeneity

  • Does it change direction of NET benefit enough to alter decisions?
  • Is it common?
  • Is it predictable?
  • Can it be detected and treatment modified in response to variation in benefits or harms?

Slide 7

Atkins 2 Slide 7. Example: SSRIs for Depression

Example: SSRIs for Depression

  • Comparable effectiveness of most agents in depression responsiveness but individual variation.
  • Affect decisions: YES—Variability in response and side effects.
  • Common: YES
  • Predictable: NO
  • Can variable response be monitored? YES

Slide 8

Atkins 2 Slide 8. Dealing With Variation in SSRI as Response in Policy

Dealing With Variation in SSRI as Response in Policy

  • Not possible to identify who will do better on a different agent.
  • Cover only 1-2 SSRIs in formulary?
  • Recommend starting all patients with a specific SSRI as initial therapy?

Slide 9

Atkins 2 Slide 9. Conclusions

Conclusions

  • Heterogeneity is a real and important phenomenon in research and policy.
  • Examination of pre-specified factors in individual trials, SRs and meta-analysis can detect HTE:
    • Be cautious about post-hoc sub-groups.
  • Policies need to accommodate HTE.
    • But doesn't mean that complete, unfettered clinician choice is best.
Current as of December 2010
Internet Citation: Going from CER to Patient-Centered Care: Implications of Heterogeneity (Text Version). December 2010. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/news/events/conference/2010/atkins2/index.html